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Journal of Environmental Economics and Management 48 (2004) 632–654
Incentives for environmental self-regulation and implications
for environmental performance
Wilma Rose Q. Anton,a George Deltas,b and Madhu Khannac,
a
Department of Economics, University of Central Florida, 302-J Business Administration II, P.O. Box 161400, Orlando,
FL 32816-1400, USA
b
Department of Economics, University of Illinois at Urbana-Champaign, 450 Wohlers Hall, 1206 South Sixth Street,
Champaign, IL 61820, USA
c
Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, 440 Mumford Hall,
1301 W. Gregory Dr., Urbana, IL 61801, USA
Received 7 October 2002; revised 9 May 2003; accepted in revised form 30 June 2003
Abstract
The increasing reliance of environmental policy on market-based incentives has led firms to shift from
regulation-driven management approaches to proactive strategies involving the voluntary adoption of
environmental management systems (EMSs). Count data and quantile regression analyses reveal that
liability threats and pressures from consumers, investors and the public are motivating EMS adoption and
that consumer pressures are particularly effective in increasing the comprehensiveness of EMSs of firms
that would otherwise be adopting a limited EMS. We also find that a more comprehensive EMS leads to
lower toxic emissions per unit output particularly for firms with higher pollution intensity in the past. EMSs
result in reductions in both off-site transfers and on-site releases per unit output. Finally, we find that
regulatory and market-based pressures do not have a direct impact on toxic releases but an indirect effect
by encouraging institutional changes in the management of environmental concerns.
r 2003 Elsevier Inc. All rights reserved.
Keywords: Environmental management systems; Environmental management practices; Environmental self-regulation;
Toxic releases; Voluntary adoption; Regulatory pressures; Market-based pressures
1. Introduction
There is a growing trend among corporations towards environmental self-regulation.
‘‘Business-led’’ initiatives such as development of firm-structured environmental management
Corresponding author. Fax: +1-217-333-5502.
E-mail address: [email protected] (M. Khanna).
0095-0696/$ - see front matter r 2003 Elsevier Inc. All rights reserved.
doi:10.1016/j.jeem.2003.06.003
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systems (EMSs), participation in trade association programs emphasizing codes of environmental
management (e.g. Responsible Care program of the American Chemical Council), and adoption
of international certification standards for environmental management, such as the International
Standards Organization are becoming widespread. These initiatives represent an internally
motivated institutional change in corporate culture and management practices towards
environmental self-regulation by incorporating environmental concerns in production decisions.
These efforts at environmental management by firms and the potential that they hold for
identifying cost-effective and self-enforcing strategies for pollution control have caught the
attention of regulatory agencies and led to a spate of programs in the US to encourage greater
adoption of EMSs [26,28,29].1 These policy initiatives are based on the presumption that EMSs
improve environmental performance; a presumption that is yet to be validated. While there is
some evidence that toxic releases emitted by firms have decreased by 43% over the 1988–97 period
(even though they are not directly regulated), the role of EMSs in achieving this reduction has not
been systematically examined2 [8,30]. Since most EMSs focus only on the means (proactive
efforts) for pollution control rather than the ends (actual performance improvement), they do not
necessarily guarantee an improvement.
This paper has two purposes. First, it examines the factors that influence the adoption of EMSs
by firms. These EMSs consist of several environmental management practices (EMPs), such as,
having an environmental policy, training and rewarding workers to find opportunities to prevent
pollution, setting corporation-wide internal standards that are maintained even by facilities in
other countries with lower environmental standards, undertaking internal environmental audits
and adopting the philosophy of total quality management (TQM) in environmental management.
Firms have considerable flexibility in the extent to which they adopt EMPs and thus the
comprehensiveness of the EMS has been observed to vary a great deal across firms [15]. We
examine the factors that explain differences in the choice of the comprehensiveness of the EMS
adopted by a sample of S&P 500 firms. The second purpose of this paper is to establish the extent
(if any) to which the comprehensiveness of the EMS has an impact on toxic release intensity of the
sample firms. In measuring this impact of EMS adoption on environmental performance, we
consider the possibility that the factors that influence the extent of EMS adoption also have a
direct impact on reducing emissions.
Our research adds to the growing literature on voluntary measures taken by firms to improve
their environmental performance (see survey in [16]). Within the empirical literature on
environmental self-regulation there are many studies examining the motivations for firms to
participate in voluntary programs established by the regulatory agency, such as the 33/50 program
1
These programs are offering technical assistance, recognition, financial and regulatory benefits to firms that
implement an EMS [9]. The interest in investigating the potential of EMSs as a policy tool can be inferred from the
broad participation in the 1999 National Research Summit on EMSs organized by a multi-state working group [23] of
eleven state environmental agency officials, the USEPA and institutions such as the Brookings Institution, the National
Academy of Public Administration and the Council of State Governments. Further information can be found at http://
www.mswg.org.
2
There is some anecdotal evidence at the firm level that self-initiated environmental management strategies are
leading to improvements in environmental performance. As part of the Voluntary Initiative for Source Reduction
spearheaded by the EPA Office of Pollution Prevention and Toxics, a Dow Chemical facility reported a reduction in
emissions of 7 million pounds as well as savings of over $5 million over a period of 2 years [30].
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[4,5,31,17], Green Lights [11,31] and WasteWise [31]. Some studies have also examined the
motivations for voluntary actions initiated by firms or trade associations unilaterally, such as
adopting an environmental plan [13] or an EMS [15], participating in the Responsible Care
program of the chemical industry [20] or adopting ISO 14001 management practices [10]. With the
exception of Khanna and Anton [15] and Dasgupta et al. [10], other studies have focused on
explaining the decision to participate or not to participate in a voluntary activity. This study, like
the above two papers, explains the observed variability in the comprehensiveness of the EMS, but
differs in that it also examines the impact of various incentives on the distribution of the count of
EMPs adopted using quantile regression methods.
The second contribution of our study is to the growing body of work examining the
implications of voluntary initiatives for environmental performance. This literature has found
mixed results. Khanna and Damon [17] find that participation in the 33/50 program led to a
statistically significant decline in releases of 33/50 chemicals while Dasgupta et al. [10] find that
adoption of ISO 14001 led to a significant improvement in the compliance status of Mexican
firms. However, King and Lenox [20] find that the rate at which members of Responsible Care
were improving their absolute and relative performance was insignificantly different from that of
non-members.
This paper shows that consumer and (possibly) investor pressure, along with potential future
liability and the scale of past emissions, are the most relevant determinants of the extent of EMS
adoption. Interestingly, our quantile regression analysis shows that the effect of consumer
pressure is stronger on the firms that would have otherwise adopted the fewest of EMPs. Using
data from the Toxics Release Inventory (TRI), we find that the extent of EMS adoption has a
significant negative impact on the intensity of toxic emissions particularly among firms with past
release intensity that exceeded that of the median firm. We found no evidence that the consumer,
investor, and future litigation risk factors that influence the comprehensiveness of EMSs have any
direct effect on toxic release intensity. Therefore, our study establishes that these factors reduce
emissions intensity indirectly only, through encouraging institutional change in the operation of
the firms.
This paper is divided into six sections. Section 2 discusses the conceptual framework that
underlies the empirical methodology developed in Section 3. Section 4 presents a discussion of the
data and variables used. Finally, results and conclusions are in Sections 5 and 6.
2. Conceptual framework
2.1. Preliminaries
EMSs represent an organizational change within corporations and an effort for self-regulation
by defining a set of formal environmental policies, goals, strategies and administrative procedures
for improving environmental performance [24]. Very often they involve applying the concept of
TQM to identifying opportunities for making continuous improvements in product quality and in
reducing pollution or manufacturing waste. As a result, EMSs have the potential to enhance the
effectiveness with which inputs are used in the production process and, since any input not
converted to output is by definition an effluent, to thereby reduce waste generation at source
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[19,25]. We assume that a rational firm chooses both the comprehensiveness of its EMS and its
level of pollution to maximize its net benefits. The costs and benefits of pollution and EMSs can
be expected to vary across firms and it would, therefore, be rational for firms to differ in the level
of pollution they generate and in the comprehensiveness of the EMS they adopt.
Our conceptual framework consists of (i) an emissions equation, which relates the ith firm’s
pollution intensity at a point in time, Yi ; to a vector of observed exogenous firm-specific variables,
Xi (that proxy for the costs and benefits of pollution generation), the comprehensiveness of its
EMS, Ei ; and unobserved factors e1i ; and (ii) an adoption equation, which relates the
comprehensiveness of the firm’s EMS, Ei ; to a vector W i (that captures the factors that influence
the benefits and costs of choosing an EMS) and unobserved factors, e2i : Some of the variables
included in W i are likely to be also included in X i : Similarly, the unobserved variables (i.e.,
disturbance terms) of the adoption and emissions intensity equations, e1i and e2i ; are also likely to
be correlated. For example, one such unobserved variable could be the ‘green’ preferences of the
current management which would affect both the choice of the EMS and the current level of
pollution intensity even after conditioning for observed variables. In order to avoid endogeneity
bias, we estimate the emissions equation using instrumental variables (IV). Since the adoption
decision is assumed not to depend on current emissions, but rather to depend on the level of past
emissions, no such endogeneity problem arises in its estimation. Details on the specification and
estimation of the adoption and emissions equations are discussed in Section 3. In the remainder of
this section, we discuss the dependent variables and the variables that form the vectors X i and W i :
2.2. Determinants of the comprehensiveness of an environmental management system
The dependent variable, Ei ; measures the comprehensiveness of the firm’s EMS and is defined
as the sum of the EMPs adopted by that firm. We now discuss the observable factors that are
expected to influence the choice of the EMS, and which should be included in W i : The existing
literature suggests that firms may undertake voluntary environmental initiatives to reduce the
costs of compliance with existing regulations, reduce the threat of regulation and shape future
regulations, improve reputation and relations with stakeholders that include consumers, investors
and communities, and in response to competitive pressure from other firms in the industry
(see [16]).
We proxy the impact of existing mandatory environmental regulations using two explanatory
variables: inspections received by firms to enforce compliance and the number of Superfund sites
for which a firm has been listed as potentially responsible. Firms are subject to periodic
inspections to enforce compliance with mandatory regulations such as the Clean Air Act and the
Clean Water Act. The variable INSPECTIONS represents the number of regulatory inspections
made on a firm. A firm that has been subjected to a higher number of inspections might face a
greater chance of receiving penalties in the future if it does not signal its ability to reduce its level
of waste generation. Firms can be held liable for contamination caused by their hazardous waste
streams under the Comprehensive Environmental Response, Compensation and Liability Act.
Firms currently listed as potentially responsible parties (PRPs) for a larger number of Superfund
sites are more likely to be aware of the liability costs of continuing to generate their past levels of
pollution. This variable is called SUPERFUND SITES.
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In addition to regulatory incentives, firms may face pressure from consumers, investors, public/
community, employees and contractors/suppliers to undertake measures to improve their
environmental management. Arora and Gangopadhyay [3] demonstrate conditions under which
consumer willingness to pay premiums for environmentally friendly products and the desire to
relax price competition for vertically differentiated products lead firms to produce ‘‘cleaner’’
products to differentiate themselves from other firms and gain market share. Participation in
voluntary programs and adoption of an EMS provides firms with a mechanism to acquire an
environmentally friendly reputation and to credibly differentiate themselves from their
competitors.3 Although there is not much direct evidence that customers are imposing pressure
on firms to adopt EMSs, many firms believe that such demands will arise in the future (see
anecdotal evidence in [14]). Firms that produce final goods and are in closer contact with
consumers are likely to feel greater pressure or benefit more from improving their environmental
friendliness. We use the 4-digit secondary SIC code to classify firms into FINAL GOOD
producers represented by a dummy variable equal to 1 if that firm is primarily selling products or
services directly to consumers (e.g. pharmaceutical preparations, cosmetics, food products).
Firms might adopt EMSs to appear less risky to investors and thus earn preferential rates on
insurance and commercial loans [2]. Several studies show that public disclosures of the TRI led to
significantly negative stock market returns for poor environmental performers [18]. Such effects
are likely to be stronger for firms with a high dependence on the capital market which we proxy by
a low SALES–ASSET ratio. This variable is also likely to be an indicator of manufacturing
activity.
Firms that have a larger volume of toxic releases (TOTAL RELEASES), defined as the sum of
on-site toxic releases and off-site transfers,4 are likely to face greater social pressure from
communities and stakeholders to undertake measures to improve their environmental
performance. Moreover, the adoption of an EMS is likely to impose fixed costs on firms
unrelated to the volume of emissions; therefore, we expect that firms with an emissions level
greater than a threshold (whose level may differ across firms) would be more likely to have
economic benefits from adoption that exceed these fixed costs. Releases in excess of a threshold
are likely to have a progressively smaller impact. We also recognize that, as total emissions
increase, the benefits to firms of adopting a more comprehensive EMS may increase, although at a
diminishing rate. For both of these reasons we use the square root of TOTAL RELEASES as an
explanatory variable.
We also hypothesize that the adoption decisions of firms are likely to be influenced by the
norms set by other firms in the industry. This could be either due to a demonstration effect as
firms learn from the experience of other adopters in their industry or due to peer pressure because
firms do not want to be singled out as laggards or environmentally unfriendly if other firms in the
industry are being more proactive. We, therefore, construct the variable OTHER EMPs by
estimating the average number of EMPs adopted by all other firms within the 3-digit SIC code of
firm. Additionally, as firms become more visible and face greater scrutiny from their stakeholders,
3
For example, the chemical industry has attempted to develop green markets by instituting the Responsible Care
trademark.
4
On-site releases include discharges to air, water, land and those injected underground while off-site transfers are
releases sent off-site for treatment, energy recovery or disposal.
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they are likely to have greater incentives to seek ways to improve their environmental reputation.
A firm’s exposure to global competitive pressures as well as domestic pressures created by greater
visibility is proxied by the number of its domestic facilities (US-FACILITIES) and the number of
facilities abroad (NON US-FACILITIES).
Adoption of an EMS is also likely to impose costs on firms because it requires greater
coordination of activities within the firm and involves employee training, audits, and product and
process improvement. Firms that are more innovative are more likely to be able to implement
changes in process and product design at a lower cost and achieve input cost savings or higher
productivity. The degree of innovativeness of firms is represented by the R&D expenditures per
unit sales (RD/SALES). Differences in the costs of implementation may also be determined by the
age of assets (AGE) as measured by the ratio between total assets and gross assets [17].5 This
variable takes a value between 0 and 1 with higher values indicating newer plant and equipment.
Firms with older assets are expected to face lower costs of replacement than firms with newer
assets.
2.3. Determinants of environmental performance
A firm’s environmental performance may be influenced both by the comprehensiveness of its
EMS and by other factors such as output levels, pressure from ‘green’ consumers, potential costs
of compliance and liabilities under mandatory environmental regulations, and firm-specific
characteristics. The adoption of an EMS could enhance efficiency of input-use and Caswell and
Zilberman [7] and Abler and Shortle [1] show that efficiency-enhancing practices reduce the ratio
of input-use per unit output and pollution per unit output. However, it is possible that firms may
only adopt the outward form of an EMS because it insulates them against stakeholder pressure or
to disguise poor performance and avoid regulatory scrutiny [20]. Additionally, in the absence of
sanctions for lack of improvement in environmental performance, firms may not follow up EMS
adoption with the effort required to really improve environmental performance [24]. Thus,
adoption of an EMS does not guarantee improvements in environmental performance and its
impact needs to be examined empirically.
We measure environmental performance here by the ratio of total emissions to total sales
(TOTAL RELEASES/SALES). This measure not only indicates whether adoption did indeed
improve the efficiency of production processes and prevent pollution, but it also allows us to scale
for differences in firm size. We also examine the impact of adoption on disaggregated emissions
using a system of three equations in which the ratios of on-site releases to sales (ONSITE
RELEASES/SALES), off-site releases to sales (OFFSITE RELEASES/SALES), and hazardous
air pollutants (HAP) to sales (HAP/SALES) are used as dependent variables. Such a
disaggregation would allow us to examine if firms target their EMSs towards reducing certain
types of pollutants or disposal methods. Additionally, HAP are the pollutants that are the most
likely to be regulated in the future. Firms have been aware since 1990 that air emissions of these
chemicals will be subject to Maximum Available Control Technology standards that would be
based on emissions levels already achieved by the best-performing similar facilities [30]. Reducing
these pollutants ahead of time using flexible methods is expected to lower the future costs of
5
Gross assets are defined as total assets plus accumulated depreciation on property, plant and equipment.
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compliance and may also give the firm a strategic advantage relative to its competitors if its
performance influences the standards that are set for other firms. Off-site disposal of toxic waste is
regulated under RCRA and can occur only in facilities that meet technology-based standards for
construction and operation set by the EPA. Firms therefore face a cost for shipping wastes off-site
and technical standards for waste treatment and disposal at the end-of-the-pipe [2]. On-site toxic
releases are currently not regulated directly but they are likely to generate greater social pressure
from communities and other stakeholders since information about them is publicly disclosed.
Any variable that influences the comprehensiveness of a firm’s EMS could also, and for the
same reasons, influence the firm’s emissions to output ratio conditional on the comprehensiveness
of adopted EMS. Therefore, current values of the variables that proxy for regulatory pressures
(SUPERFUND SITES), market pressures (FINAL GOOD, SALES–ASSETS ratio), and firmspecific characteristics (AGE, US-FACILITIES, NON US-FACILITIES, R&D/SALES) are
included as regressors in the performance equations.6 This allows us to distinguish between the
direct and indirect effect (through EMS adoption) of these factors on pollution reduction.
We also include the 5 year lagged TOTAL RELEASES/SALES as an explanatory variable to
capture persistence in pollution intensiveness that may exist due to the partial fixity of the capital
equipment and the underlying technology, that is, as a control for the residual effects from other
unobservable variables that cause heterogeneity among firms and affect emissions.
The endogenous explanatory variable in the regression is the comprehensiveness of a firm’s
EMS (the number of EMPs adopted by a firm). In one model we also use an interaction of
count of EMPs with lagged TOTAL EMISSIONS/SALES as an explanatory variable to see
if the impact of EMSs on more polluting firms was different from that on less polluting
firms. The instruments for these endogenous variables consist of all the regressors hypothesized to be determinants of comprehensiveness of the EMS. Next, we discuss the empirical
methodology.
3. Empirical framework
3.1. Determinants of the comprehensiveness of the environmental management system
We measure the impact of market and (anticipated) regulatory pressures, W i ; on the
comprehensiveness of the EMS using standard Poisson and Negative Binomial models [6]. We
complement this analysis with the use of semiparametric, quantile regression-based methods
[21,22]. The Poisson analysis estimates the expected number of EMPs as a function of firm
characteristics. The Negative Binomial models provide an independent estimate of the variance of
EMPs. Neither of these two methods provides a direct estimate of the impact of firm
characteristics on the distribution of EMPs. While these models do imply a distribution for EMPs,
this distribution is derived directly from the estimates of the mean and variance of EMPs and does
not contain any additional information. For example, under the Poisson model, if an element of
6
We did not include the current number of inspections because of endogeneity concerns as the number of inspections
could be related to emissions intensity. We did not have a good instrument for the number of current inspections, and in
any case, the variable did not have a significant effect on adoption or on emissions-intensity.
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W i increases the expected number of EMPs, it will (proportionately) scale the entire distribution
of EMPs.
On the other hand, quantile regressions provide an estimate of the effect of the regressors on
each of the quantiles of the distribution of EMPs. They can show, for example, if a variable leads
to an increase in the average number of EMPs adopted by firms by shifting the entire distribution
of EMPs upwards, without changing its shape (indicating that all firms are equally affected by
that variable), or by ‘‘stretching’’ the distribution of EMPs upwards, leaving the EMPs of firms in
the lower tail unchanged and increasing the number of EMPs for firms in the upper tail (consistent
with the hypothesis that low adopters either have little benefits from adoption or have high costs
from adopting) or by ‘‘compressing’’ the distribution of EMPs upwards (consistent with the
hypothesis that an increase in an explanatory variable wi provides greater returns for adoption to
low adopters). In particular, we directly estimate the tth quantile of Ei ; Qt ðEi Þ; assuming that the
quantiles are a linear function of the vector of observed characteristics, W i ; using the quantile
regressions:
Qt ðEi Þ ¼ gt W i :
ð1Þ
By estimating quantile regressions for a continuum of t we obtain estimates of the conditional
distribution of Ei as a function of the characteristics, W i : Denote the predicted, conditional on
W i ; value of Ei at the tth quantile by Q̂t ðEi jW i Þ: We calculate Q̂t ðEi jW i Þ for this fine grid of t;
then plot the distribution of Ei in the form of a histogram. We use the quantile regressions results
to plot the counterfactual distribution of EMPs if firms were to be endowed with different values
of a regressor (holding the values of the other regressors constant at some particular level). This
allows us to determine how changes in the regressors affect the entire distribution of Ei :
3.2. The analysis of the environmental performance of firms
We estimate the impact of EMS adoption and other factors on aggregate releases per unit sales
using IV methods. Further, to examine if EMS adoption has a differential impact on different
types of pollutants or methods of disposal, we disaggregate total toxic releases into those emitted
on-site, those transferred off-site, and HAP and estimate a system of three equations using
Generalized Method of Moments (GMM). These equations are:
yim ¼ X im b2 þ am Ei þ eim ;
i ¼ 1; y; I;
m ¼ 1ðon-siteÞ; 2ðoff-siteÞ; 3ðHAPÞ:
ð2Þ
The covariance between the errors across pairs of equations, eim and ein equals smn where
m; n ¼ 1; y; 3 which is assumed to be homoskedastic and i.i.d. when m ¼ n and is non-zero when
man; implying that the errors across equations are correlated.
The main purpose of the disaggregated analysis is to investigate whether increasing the
comprehensiveness of EMS has a differential impact on each type of emissions. Given that the
scale of each type of emissions differs (HAP are a subset of on-site air releases, and off-site
releases are of lesser magnitude than on-site releases), testing for equality of the response to
improvements in EMS is facilitated by normalizing the emissions to output ratio of a particular
type by the average emissions to output ratio of that type. Note that this normalization does not
affect the level of significance of the regressors, but only rescales them so that they are comparable
across equations. To maintain consistency and facilitate the interpretation of results, we perform a
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similar normalization for the aggregate emissions to output ratio in the single-equation regression
and also for aggregate emissions in the count model regressions.
We recognize that a subset of the explanatory variables in our equations is endogenous. This
includes the comprehensiveness of the EMS, Ei ; that the firms have adopted and any of its
interactions with exogenous regressors. To deal with this endogeneity problem, we use a set of
instruments, i.e., a set of variables that is correlated with the comprehensiveness of the EMS and
uncorrelated with the disturbance terms. Let the combined vector of the exogenous explanatory
variables and the instruments be zi which, by definition, satisfies the orthogonality conditions
E½zi eim ¼ 0 ) E½zi ðyim bm Xi Þ ¼ 0;
ð3Þ
where yim denotes the dependent variable in equation m: The sample analog of the above
orthogonality conditions is given by
T
1X
zi ðyim bm Xi Þ ¼ 0:
T i¼1
ð4Þ
The left-hand side of the system of equations is the vector of sample moments. The parameter
vectors bm are estimated by choosing the values b# m that minimize a weighted sum of the squared
sample moments. We use the inverse covariance matrix of the disturbance term as the weights in
the minimization problem, which results in the three-stage least-squares (3SLS) estimates for the
system of equations in (2) (see [12]). Unlike the ‘‘traditional’’ 3SLS estimation procedure,
endogenous variables do not appear as dependent variables in any of the equations we estimate.
However, the estimation procedure is identical to 3SLS in terms of the mechanics. For the single
equation model in which the dependent variable is the ratio of total emissions to output, this
procedure collapses to standard IV (or 2SLS) estimation. Our choice of the instrument vector, z; is
based on the analysis of the determinants of the comprehensiveness of an EMS. In particular, all
elements of W i are used as instruments for the system of Eq. (2).
4. Data
This study relies on firm-level data on EMPs for S&P 500 firms included in the Corporate
Environmental Profile Directories compiled from firm surveys by the Investor Research
Responsibility Center for 1994–1995. The survey inquires about the adoption decision of firms
for 13 EMPs described in Table 1.
Additionally, we use primary data on environmental performance for 1994–1995 obtained from
the TRI database, which contains facility-level information on chemical-specific toxic emissions.
The TRI, which was first released in 1989, is mandated by the Emergency Planning and
Community-Right-to-Know Act of 1986 and requires production facilities to report annual
quantities of on-site toxic emissions to various media and the quantities of off-site transfers. These
data are aggregated across chemicals and facilities of each parent company to obtain total on-site
toxic releases and offsite transfers at the parent company level. We also obtain data at the parent
company level on the volume of the 189 pollutants identified as HAP by the Clean Air Act
of 1990.
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Table 1
Description and adoption of environmental management practices
Variable
Mean values
Description of the variable (1=yes; 0=no)
Staff
Directors
Policy
0.49
0.47
0.93
Corp. stds.
TQEM
0.40
0.71
Payments
0.62
Audits
0.92
Suppliers
Partners
Clients
Report
0.53
0.41
0.11
0.39
Reserves
0.48
Insurance
0.44
Firm has an environmental staff of more than 50
Firm has more than 3 environmental directors
Firm has a formal written policy and codes of conduct on environmental
issues
Firm applies uniform standards to environmental practices worldwide
Firm applies principles of total quality management to environmental
problems
Firm provides incentive compensation to employees whose efforts lead to
achievement of specific environmental goals
Firm conducts audits to assess compliance with environmental
regulations
Firm evaluates its environmental risks when selecting its suppliers
Firm evaluates its environmental risks when selecting its partners
Firm evaluates its environmental risks when selecting its clients
Firm regularly releases reports about its environmental performance and
activities
Firm sets aside funds to cover the costs of penalties for environmental
violation or remediation activities
Firm purchases insurance to meet unexpected environmental liabilities
Information on regulatory inspections made on firms to check compliance with various
environmental statutes is obtained from EPA’s publicly available Integrated Data for
Enforcement Analysis. Data on the number of Superfund sites for which firms are held
potentially liable are obtained from EPA’s Site Enforcement Tracking System [27]. Facilities
(subsidiaries and divisions) within each parent company are identified by using Ward’s Business
Directory of US Private and Public Companies [32]. Financial variables, such as net sales, R&D
expenditures, and net and gross assets, are obtained from the Standard & Poor (S&P) 500 and
Super Compustat databases which provide information on all publicly traded firms that file 10-K
forms with the Securities and Exchange Commission.
In the adoption decision equation, all time dependent explanatory variables are measured with
a 5-year lag (i.e., for the years 1989 and 1990) since the adoption of some practices may have
occurred prior to 1994 or 1995 and since the adoption decision is likely to depend on past levels of
firm attributes. For the environmental performance regressions, pollution intensity (the dependent
variable) is measured in 1994 and 1995. The time-dependent explanatory variables are measured
contemporaneously. From the original 500 firms included in the 1994 and 1995 survey of S&P 500
firms we included only those firms that responded to the surveys, for which financial performance
data were available for the years 1989, 1990, 1994 and 1995, and that emitted non-zero toxic
emissions for at least one of the these 4 years.
Tables 2 and 3 show the descriptions of the variables used in the study and their mean values. A
pooled sample for the 2-year data were created for a total of 313 observations: 149 firms for 1994
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Table 2
Description and descriptive statistics of the dependent variables
Dependent variables
Description
EMPt
Number of environmental management
practices adopted
Total toxic emissions–sales ratio (pounds per
dollar)
On-site discharges of toxic emissions (pounds
per dollar)
Off-site transfers of toxic emissions for energy
recovery, recycling, disposal (pounds per
dollar)
HAP–sales ratio (pounds per dollar)
TOTAL RELEASESt/
SALESt
ONSITEt/SALESt
OFFSITEt/SALESt
HAPt/SALESt
Mean
Std. dev.
6.92
3.22
1316.82
4343.89
659.18
2407.09
657.64
2378.53
256.19
543.26
and 164 firms for 1995 with 146 firms with observations that were common across both years.7 Of
these 146 firms, 60 firms increased the number of EMPs they adopted while 12 firms decreased
them. The average number of practices adopted for the sample increased from 6.6 to 7.2. The
change in the number of EMPs between the 2 years in our sample is much smaller than the crosssectional variance in the number of EMPs. Therefore, it is the cross-sectional variation that
provides most of the identifying power. Note that the distribution of lagged toxic releases per unit
sales ranged from zero to 37,569 pounds per dollar of sales with 75% of the observations having a
ratio less than the mean of 1661 pounds per dollar; the standard deviation of the distribution is
3928. This indicates a highly positively skewed distribution. The distribution of current toxic
releases per unit sales is similarly positively skewed. As we discuss below, this has implications for
the interpretation of the results and for our choice of specifications.
5. Results
5.1. Determinants of the comprehensiveness of environmental management
5.1.1. Results of Poisson models
We estimate five different specifications of the Poisson model to examine the determinants of
the comprehensiveness of the EMS adopted by the firms in our sample.8 Models 1P, 2P and 3P are
estimated by pooling the data for 1994 and 1995. Models 1R and 3R have the same specifications
as 1P and 3P, respectively, but are estimated using a random effects specification that recognizes
the panel nature of our data (Table 4). The w2 test for firm random effects in Models 1R and 3R
fails to reject the presence of persistence in the comprehensiveness of EMS at the firm level.
7
In models that use the variable, OTHER-EMPs, the sample consists of 242 observations. This is because firms in 3digit SIC codes which consist of only one firm had to be eliminated.
8
The likelihood ratio test for over-dispersion fails to reject the Poisson relative to the Negative Binomial models at
the 1% level. The results of the two models are almost identical.
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Table 3
Description and descriptive statistics of the independent variables
Explanatory
variables/instruments
Description
Mean
Std. dev.
TOTAL
RELEASEt
5
Total toxic emissions (on-site emissions+off-site
transfers) (’000 pounds)
11,200
31,900
TOTAL RELEASES/
SALESt
5
Total toxic emissions–sales ratio (pounds per dollar)
1660.84
ONSITEt
5
On-site discharges of toxic emissions (’000 pounds)
9678.41
OFFSITEt
5
Off-site transfers of toxic emissions for energy recovery,
recycling, disposal (’000 pounds)
1518.18
3533.78
HAPt
5
Hazardous air pollutants (HAP) subject to NESHAP
regulations in year 2000 (’000 pounds)
3352.12
7293.80
OTHER-EMPs
Average number of environmental management
practices adopted by all other firms in the sample within
the same 3-digit SIC code
6.91
2.32
SUPERFUND
SITESt
Accumulated number of Superfund sites for which a firm
is identified and potentially held responsible
22.79
30.08
13.85
17.87
33.97
52.82
SUPERFUND
SITESt
5
3928.06
29,400
INSPECTIONSt
5
Number of regulatory compliance inspections received
from the EPA
FINAL GOOD
Dummy (=1 if firm sells final goods; =0 otherwise)
0.62
0.49
SALES/ASSETt
Sales–total assets ratio
1.08
0.46
1.13
0.49
SALES/ASSETt
5
NON USFACILITIES
Number of facilities overseas
29.85
43.92
US-FACILITIES
Number of facilities in the US
28.53
30.41
R&D/SALESt
R&D expenditures–sales ratio
0.03
0.04
0.03
0.04
0.74
0.11
0.77
0.10
R&D/SALESt
5
AGE OF ASSETSt
Gross assets/total assets
AGE OF ASSETSt
5
Subscript t refers to 1994 and 1995 and t 5 refers to 1989 and 1990.
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Table 4
Determinants of the comprehensiveness of environmental management: Poisson regression models
Variables
INTERCEPT
INSPECTIONSt
5
SUPERFUND SITESt
5
FINAL GOOD
TOTAL RELEASESt
5 (sqrt)a
Model 1P
Model 2P
2.11
(0.201)
0.0003
(0.0006)
0.003
(0.002)
0.264
(0.049)
0.089
(0.038)
1.56
(0.261)
0.0008
(0.0007)
0.005
(0.002)
0.270
(0.058)
0.158
(0.052)
0.0004
(0.0008)
0.0009
(0.0006)
0.399
(0.233)
1.38
(0.672)
0.218
(0.054)
0.000006
(0.001)
0.0006
(0.0006)
0.017
(0.273)
0.897
(0.768)
0.131
(0.075)
0.02
(0.01)
FINALGOOD TOTAL RELEASESt
5
US-FACILITIES
NON US-FACILITIES
AGE OF ASSETSt
5
R&D/SALESt
5
SALES/ASSETSt
5
OTHER-EMPst
Ln Alpha
Alpha
N
Log L
w2 fp-valuegb
w2 fp-valuegc
Pseudo-R2
Model 3P
2.00
(0.208)
0.00005
(0.0006)
0.003
(0.002)
0.373
(0.070)
0.219
(0.069)
0.166
(0.074)
0.0004
(0.0008)
0.0009
(0.0006)
0.385
(0.233)
1.80
(0.700)
0.212
(0.054)
Model 1R
2.15
(0.305)
0.00007
(0.0009)
0.003
(0.002)
0.272
(0.071)
0.082
(0.061)
0.0004
(0.001)
0.001
(0.0009)
0.444
(0.355)
1.30
(1.02)
0.231
(0.077)
Model 3R
2.03
(0.310)
0.0002
(0.0009)
0.004
(0.002)
0.398
(0.100)
0.227
(0.100)
0.200
(0.110)
0.0005
(0.001)
0.001
(0.0009)
0.446
(0.353)
1.77
(1.04)
0.227
(0.077)
2.33
2.36
(0.234)
(0.236)
0.097
0.095
(0.023)
(0.022)
313
242
313
313
313
776.59
612.33
774.16
752.36
750.71
139.15{0}
95.41{0}
144.02{0}
61.84{0}
65.89{0}
0.77{0.19}
1.71{0.095}
0.45{0.25}
48.47{0}d
46.89{0}d
0.079
0.072
0.084
Standard errors are in parentheses.
Statistically significant at the 10% level; statistically significant at the 5% level; statistically significant at the 1%
level; statistically significant at the 20% level.
a
Total releases for each firm are normalized so that the average of total releases is one.
b 2
w {p-value} is a test for all slope coefficients jointly equal to zero.
c 2
w {p-value} is a test for the null hypothesis that the Poisson model is appropriate.
d
This is the likelihood ratio test statistic of alpha=0.
Random effects estimation does not affect the consistency of parameter estimates but it does
provide correct standard errors under the null of gamma distributed random effects.
All regression models are consistent in showing that firms that were listed as PRPs for a larger
number of Superfund Sites and thus faced a stronger threat of future liabilities are more likely to
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645
adopt a more comprehensive EMS, though its significance is lower in the random effects
specifications. This is consistent with the hypothesis that environmental regulations do push firms
towards being more proactive in managing their environmental performance. In contrast, the
impact of the number of EPA inspections on EMS adoption is statistically insignificant. The lack
of a positive impact of inspections indicates that EMSs are not being adopted to obtain a lenient
treatment with regard to compliance with existing regulations.
The pooled and the random effects models are also consistent in showing that larger polluters,
measured by the squared root of total (normalized) toxic releases, are significantly more likely to
adopt a more comprehensive EMS even though toxic releases are currently not directly penalized
by mandatory regulations. This could be either to exploit the potential for cost-savings by
reducing waste through proactive environmental management or in response to the performance
of peer firms and the desire to avoid potentially adverse stakeholder reactions. Our hypothesis
that the effect of emissions on the comprehensiveness of the EMS is likely to be characterized by a
threshold and diminishing returns turns out to be correct: the log-likelihood of the specification in
which the square root of emissions is used as a regressor is substantially higher than that of the
specification using the linear measure of the variable.9 In order to confirm that the volume of toxic
releases was not simply an indicator of firm size, we also estimated the models in Table 4 with
lagged sales as an explanatory variable (results are not reported here for brevity). We found that
the effect of the volume of total releases continued to be significant and positive even after we
include sales as a variable while sales itself had an insignificant impact on the adoption decision.
The significance of the remaining variables remains unchanged. The impact of peer pressure on
adoption is also confirmed by Model 2P which shows that adoption of a large average number of
EMPs by other firms in the same 3-digit SIC code has a statistically significant positive impact on
the number of practices adopted by a firm.
Our results also support the hypothesis that consumer pressure has a direct impact on a firm’s
management strategies. Firms in closer contact with consumers are likely to be more
environmentally proactive. This result corroborates findings in other studies [4,13,15,17].
Furthermore, both the pooled data Model 3P and the random effects Model 3R show that the
interaction term between the variable final good and total releases emitted by a firm has a
statistically significant negative effect on adoption. This indicates that, among smaller polluters,
final good producing firms are more likely to adopt a more comprehensive EMS. This suggests
that while larger polluters are motivated to adopt a more comprehensive EMS for a variety of
other reasons, consumer pressure is important in inducing firms that would otherwise adopt a
limited EMS.10 Furthermore, all the specifications in Table 4 show that firms with a high capital–
output ratio (or a low sales–asset ratio), and thus more vulnerable to investor sentiment, are more
likely to adopt a more comprehensive EMS. Parameter estimates of explanatory variables, age,
non-US facilities and R&D/sales, have the expected sign but their significance is not robust across
specifications.
9
The difference in the log-likelihood between the two models, which is a lower bound of the difference in the loglikelihood of a model is which the degree of concavity is estimated, is significant on the a basis of a likelihood ratio test
with one degree of freedom.
10
Other measures of competitive pressure, such as the concentration of the industry as measured by the Herfindahl–
Hirschman index, were not found to have a significant impact on the adoption decision.
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Table 5
Determinants of comprehensiveness of environmental management: quantile regressions
Variables
Q10
Q30
Q50
Q70
Q90
INTERCEPT
1.89
(3.60)
1.57E
03
(0.01)
0.03
(0.03)
2.57
(0.59)
0.82
(0.56)
0.01
(9.27E
03)
8.32E
03
(7.09E
03)
1.66
(4.05)
11.56
(10.13)
0.75
(0.56)
0.1887
6.48
(2.46)
1.62E
03
(6.84E
03)
0.03
(0.01)
2.38
(0.46)
0.21
(0.59)
9.06E
05
(0.01)
8.62E
03
(6.00E
03)
3.01
(2.56)
10.97
(9.90)
1.19
(0.53)
0.1885
6.85
(1.67)
3.76E
03
(5.95E
03)
0.02
(0.01)
1.82
(0.36)
1.16
(0.55)
3.98E
03
(8.88E
03)
4.35E
03
(4.67E
03)
0.63
(2.03)
6.92
(7.10)
1.56
(0.46)
0.1919
10.63
(2.02)
5.37E
03
(7.67E
03)
0.02
(0.02)
1.21
(0.44)
0.76
(0.51)
5.06E
03
(5.68E
03)
8.35E
04
(5.47E
03)
3.43
(2.23)
17.37
(5.42)
1.57
(0.32)
0.1600
10.52
(1.34)
0.02
(6.13E–03)
8.44E
03
(0.01)
1.08
(0.42)
0.48
(0.39)
0.02
(7.85E
03)
9.31E
03
(4.66E
03)
1.43
(1.90)
1.05
(5.18)
0.42
(0.53)
0.1932
INSPECTIONSt
5
SUPERFUND
SITESt
5
FINAL GOOD
TOTAL RELEASESt
5 (sqrt)a
US-FACILITIES
NONUS-FACILITIES
AGE OF ASSETSt
5
R&D/SALESt
5
SALES/ASSETSt
5
Pseudo-R2
Standard errors are in parentheses.
Statistically significant at the 10% level; statistically significant at the 5% level; statistically significant at the 1%
level; statistically significant at the 20% level.
a
Total releases for each firm are normalized so that the average of total releases is one.
5.1.2. Results of quantile regression analysis
We use quantile regression methods to examine how the explanatory variables considered affect
the distribution of EMPs. This distribution is not degenerate even after conditioning on all observed
characteristics because firms differ in unobserved ways that are relevant to the comprehensiveness of
their EMS. We refer to these ‘‘residual’’ unobserved differences as the propensity to adopt a more
comprehensive EMS. Thus, the quantile regression results can be interpreted as investigating how
the effect of the explanatory variables differs with respect to this unobserved propensity to adopt.
The most important finding of this analysis, using the same specification as Model 1P of Table 4
(reported in Table 5), is that closeness to consumers not only has a statistically significant effect, but
also one that is stronger for lower quantiles. In other words, the effect of being a final goods
producer is stronger for firms with a relatively lower propensity to adopt a more comprehensive
EMS (conditional on their other characteristics). The effect of the number of Superfund sites is
broadly constant across quantiles, though it somewhat falls with the quantile level. There is no
apparent relationship between the magnitude of the other coefficients and the quantile level.11 The
11
The results of quantile regressions using the same variables as Models 2 and 3 of Table 4 show effects that are
qualitatively similar to those of the corresponding Poisson regressions and are omitted for brevity.
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647
Fig. 1. Conditional distribution of EMPs.
effect of being a final goods firm on the distribution of the count of adopted practices is examined
graphically in Fig. 1 for two types of firms: a low toxic release emitter and a high toxic release
emitter. In order to separately identify the effect of final good for the two types of firms we
construct the figure using the results of quantile regressions specified as in Model 3P of Table 4
(that is including the interaction between final good and the square root of total emissions).
The top left panel of Fig. 1 shows the distribution of EMPs for a firm with characteristics equal
to that of the average firm in the sample, except that (i) its emissions are equal to the 10th
percentile of the unconditional distribution of emissions and (ii) it is not a final goods producer.
The top right panel of Fig. 1 shows the distribution of EMPs for the same set of firms had all of
these firms been final goods producers. The upward shift in the support of the distribution is
minimal. However, the shape of the distribution has changed radically: the bottom tail has
thinned substantially, and the distribution exhibits a clear mode and a fatter upper tail. Clearly,
among low emitters, the impact of being of a final goods producer is positive because final goods
producers are very unlikely to be at the extreme low end of the distribution. This suggests that the
effects of consumer-induced discipline are concentrated on firms that ‘‘fail to make the grade’’,
i.e., if a firm is a laggard, it is likely to attract unwanted attention, but beyond a certain point it no
longer obtains additional benefits from further adoption of EMPs. The bottom panels of Fig. 1
repeat this exercise, but condition on the emission level being equal to the 90th percentile of the
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distribution of emissions. A few observations are in order. First, high emitters adopt a higher
number of EMPs even conditional on not being final goods producers. Second, the effect of being
a final goods producer is qualitatively similar for this group of firms as it is for the low emitters,
but quantitatively less pronounced. Not only is the increase in the average number of EMPs
smaller, but the thinning of the lower tail is less pronounced. This may be because high emitters
probably have sufficiently strong incentives to adopt at least some EMPs.
5.2. The impact of the comprehensiveness of EMS on environmental performance
We now examine the impact of the comprehensiveness of environmental management on the
environmental performance of firms emitting toxic pollution. In order to test the robustness of the
results, a number of specifications are estimated with different sets of explanatory variables and
instruments. In Table 6, we present the results of the regression models with environmental
performance measured by the ratio of aggregate toxic releases per unit sales (normalized so that
the average value of that ratio equals 1). Model 1 uses OLS and ignores the endogeneity of the
EMS adoption decision. This model shows that count of EMPs adopted had an insignificant
impact on environmental performance possibly because firms with a high propensity to pollute are
also more likely to adopt a more comprehensive EMS (thus biasing the coefficient of EMS
upwards). We test the null hypothesis that the number of EMPs is exogenous using the Hausman
Test [12]. We find that when we use only the regressors that are significant, we reject the null
hypothesis of non-endogeneity; however, if we use all the regressors, we fail to reject the null
hypothesis. This could be due to the fact that regressors which have no impact on the dependent
variable are likely to have no significant effect on both the efficient and inefficient models and thus
do not affect the vector of differences in the coefficients of the two models. They do, however,
affect the degrees of freedom of the w2 test and therefore make it harder to reject the null
hypothesis. Therefore, we use IV for the remaining models as discussed in Section 3.12
Model 2 shows that the comprehensiveness of the EMS has a statistically significant negative
impact on total releases per unit sales. It also shows that the lagged level of total releases per unit
sales has a very strong positive influence. We also find that the time trend variable has a significant
positive effect, though the effect is not robust across specifications. Firms with a lower sales–asset
ratio have a higher ratio of toxic releases per unit sales. Neither the other market pressure variable
(FINAL GOOD), nor the threat of liabilities is statistically significant. While innovativeness of
the firm and newer assets have a negative effect on releases per unit sales, the effect is not
statistically significant. The coefficient of the EMS variable in Model 2 shows that an incremental
change in the number of practices adopted lowers the pollution intensity by an amount that is
equal to 79% of the average firm’s current emissions intensity (since current pollution intensity
has been normalized to be one for an average firm). Observe that this effect appears large partly
because it is the effect on the ‘‘treated’’ firms (i.e., the adopters) as a percentage of the emissions
intensity of the average firm (including the adopters) rather than as a percentage of the intensity of
12
The standard errors reported in Tables 6 and 7 are asymptotic standard errors. We also computed boot-strapped
standard errors by resampling entire histories of firms so as to account for the possibility of persistence in the residual.
This approach is conservative in that it assumes perfect persistence. The boot-strapped standard errors are somewhat
higher but overall significance of the results is not affected.
Table 6
Determinants of aggregate toxic releases per unit sales
Model 1
Model 2
Model 3
Model 4
OLS
IV exclude OTHER-EMPs as instruments
Model 5
IV include OTHER-EMPs
Total releases/salest
5a
p0.245b
INTERCEPT
EMPst
465.15
(524.53)
0.021
(0.048)
EMPs TOTAL-RELEASE=SALESt
5
SUPERFUNDSITESt
FINAL GOOD
AGE OF ASSETSt
YEAR
US-FACILITIES
NON US-FACILITIES
R&D/SALESt
SALES/ASSETSt
N
F-test [p-value]
0.988
(0.056)
0.002
(0.005)
0.684
(0.290)
0.959
(1.27)
0.233
(0.263)
0.003
(0.005)
0.001
(0.004)
7.56
(3.99)
0.210
(0.302)
313
0
1616.20
1664.93
96.213
1908.82
(797.61)
(809.121) (114.89)
(1322.29)
0.785
0.686
0.028
0.760
(0.246)
(0.295)
(0.028)
(0.340)
0.071
(0.003)
1.02
1.69
1.44
0.991
(0.077)
(0.935)
(0.366)
(0.103)
00.013
0.013
0.002
0.011
(0.008)
(0.008)
(0.001)
(0.012)
0.674
0.682
0.012
0.007
(0.578)
(0.584)
(0.077)
(0.858)
1.278
1.350
0.239
0.734
(1.86)
(1.88)
(0.275)
(3.33)
0.814
0.838
0.048
0.961
(0.400)
(0.406)
(0.058)
(0.664)
0.004
0.004
0.001
0.010
(0.007)
(0.007)
(0.001)
(0.011)
0.007
0.006
0.0003
0.010
(0.006)
(0.006)
(0.001)
(0.013)
1.54
1.08
0.712
0.806
(5.74)
(5.84)
(0.727)
(13.90)
1.38
1.22
0.085
2.45
(0.549)
(0.613)
(0.057)
(1.31)
313
313
157
156
0
0
0.019
0
953.94
(661.79)
0.389
(0.178)
1.29
(0.069)
0.012
(0.006)
0.243
(0.480)
0.610
(1.56)
0.479
(0.332)
0.002
(0.006)
0.002
(0.004)
5.22
(4.71)
0.542
(0.540)
242
0
Standard errors are in parentheses. The F-test is the test of significance of the regression.
Statistically significant at the 10% level; statistically significant at the 5% level; statistically significant at the 1% level.
a
Total releases/sales for each firm is normalized so that the average is one.
b
This is the median level of (normalized) 5 year lagged total releases per unit sales.
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Variables
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the non-adopters.13 Further, it is possible that the effect of increasing the comprehensiveness of
the EMS is higher for firms with higher pre-adoption pollution intensity. Therefore, when the
benefits are compared to the emissions intensity of the average firm, the extent of reduction
appears to be high.
In Model 3 we attempt to control for this latter effect by including the interaction between
count of EMPs and lagged toxic releases per unit sales as an endogenous explanatory variable.
The coefficient of this interaction variable has a negative sign (suggesting that adoption had a
larger effect on more pollution intensive firms) but it is not statistically significant. One possibility
for the lack of significance may be the skewness in the distribution of emissions intensity: if there
are any mild departures from linearity in the response of current total releases/sales to the
interaction between EMPs and past total releases/sales, then this would manifest itself in a poor fit
and in high standard errors.
To further investigate the differential effect of adoption on firms of different emission
intensities, we split the sample in two equal groups and estimate Model 3 for each of the subsamples (Model 4). Sub-sample 1 includes firms with a total releases/sales ratio of less than or
equal to the sample median while sub-sample 2 includes the rest of the sample. Our analysis
reveals a striking difference between the magnitude and the significance of the coefficient of EMPs
for the two sub-samples. The effect of EMPs on firms with low pollution intensity in the past is
negative but small and insignificant while on firms with high pollution intensity is large and
significantly negative. This suggests that EMPs were beneficial in increasing efficiency and
reducing waste generation at source, particularly among the more pollution intensive firms. In
Model 5 we include OTHER-EMPs as an instrument (and consequently lose a number of
observations) and find that the results remain the same as that obtained by Model 2. The
comprehensiveness of the EMS has a negative significant effect on toxic releases per unit sales.
The magnitude of the effect predicted by this model is, however, smaller than that predicted by
Model 2.
With regards to the remaining variables, all of the IV specifications estimated here are
consistent in showing that market and regulatory pressures do not directly influence
environmental performance. Rather, they play an indirect role in improving environmental
performance by inducing adoption of a more comprehensive EMS. Their entire effect is embodied
in institutional change and there is no incremental effect on toxic releases once this institutional
change is accounted for.
Finally, we examine the effect of adoption of an EMS on the intensity of different types of toxic
releases by disaggregating total releases into those emitted on-site, those transferred off-site, and
HAP. The results in Table 7 show the effect of adoption on ratios of these three categories of
pollutants per unit sales for firms with past pollution intensity lower than that of the median firm,
and for firms with past pollution intensity higher than that for the median firm. To maintain the
full sample, we exclude OTHER-EMPs as an instrument. For firms that were more pollution
13
For example, suppose that the EMS adoption is described by a binary variable and consider a pool of
homogeneous firms, 90% are adopters and 10% are non-adopters. Further suppose that adopters have emission
intensity of zero, while non-adopters have emission intensity of 10. In a cross-section data set, the average emission
intensity equals one and the coefficient on adoption equals 10, which is much higher than the intensity of the average
firm in the sample.
Variables
INTERCEPT
EMPst
TOTAL RELEASES/SALESt
5a
FINAL
AGE OF ASSETSt
YEAR
US-FACILITIES
NONUS-FACILITIES
R&D/SALESt
SALES/ASSETSt
N
w2 {p-value}
TOTAL RELEASES/SALESt
5
40.245b
ONSITE/SALES
OFFSITE/SALES
HAP/SALES
ONSITE/SALES
OFFSITE/SALES
HAP/SALES
1.24
(30.57)
0.011
(0.007)
0.725
(0.097)
0.0001
(0.0004)
0.022
(0.204)
0.025
(0.073)
0.001
(0.015)
0.0003
(0.0003)
0.0003
(0.0002)
0.323
(0.193)
0.009
(0.015)
157
101.05
{0}
193.90
(217.09)
0.064
(0.052)
2.158
(0.691)
0.003
(0.003)
0.002
(0.145)
0.504
(0.519)
0.097
(0.108)
0.003
(0.002)
0.001
(0.002)
1.10
(1.373)
0.161
(0.108)
157
16.00
{0.100}
4.12
(44.57)
0.0184
(0.011)
1.063
(0.142)
0.0005
(0.0006)
0.010
(0.030)
0.064
(0.106)
0.002
(0.022)
0.0005
(0.0005)
0.0004
(0.0003)
0.631
(0.282)
0.019
(0.022)
157
89.18
{0}
1607.23
(1303.40)
0.714
(0.335)
1.23
(0.102)
0.009
(0.012)
0.942
(0.846)
0.012
(3.286)
0.810
(0.654)
0.015
(0.011)
0.008
(0.0130)
6.60
(13.69)
3.44
(1.29)
156
178.40
{0}
2211.12
(1544.57)
0.806
(0.397)
0.753
(0.121)
0.013
(0.014)
0.959
(1.002)
1.481
(3.894)
1.113
(0.775)
0.005
(0.013)
0.004
(0.015)
8.23
(16.22)
1.47
(1.53)
156
59.73
{0}
535.07
(823.03)
0.161
(0.212)
0.143
(0.064)
0.010
(0.007)
1.771
(0.534)
0.974
(2.075)
0.270
(0.413)
0.011
(0.007)
0.001
(0.008)
26.24
(8.66)
1.80
(0.814)
156
47.53
{0}
Standard errors are in parentheses.
Statistically significant at the 10% level; statistically significant at the 5% level; statistically significant at the 1% level.
a
Total releases/sales are normalized for each firm so that the average is equal to one.
b
This is the median level of (normalized) 5 year lagged total releases per unit sales.
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TOTAL RELEASES/SALESt
5a
p0.245b
W.R.Q. Anton et al. / Journal of Environmental Economics and Management 48 (2004) 632–654
Table 7
Determinants of disaggregated toxic releases per unit sales: split sample results
651
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intensive, we find that adoption of a more comprehensive EMS has a significant negative impact
on their on-site releases per unit sales and on off-site transfers per unit sales but an insignificant
(although negative) impact on their HAP per unit sales. This could imply that EMSs were targeted
broadly towards improving environmental performance and not towards reducing specific types
of pollutants. We fail to reject the null that the magnitude of the coefficients of the EMS variable
in the on-site release intensity and off-site release intensity models are the same. This implies that
EMSs had a similar effect on on-site release intensity and on off-site release intensity. We also find
that adoption of a more comprehensive EMS has no effect on any of the different types of releases
per unit sales for firms that were less pollution intensive. For all firms, however, higher past
pollution intensity contributes to higher current pollution intensity for all emission categories.
This effect is significantly larger on current on-site release intensity as compared to off-site release
intensity and HAP per unit sales. Consumer pressure has a positive but insignificant impact on
on-site release intensity and a negative but insignificant impact on off-site release intensity.
Surprisingly it has a positive and significant effect on HAP/sales. This could indicate that firms
were able to use their EMSs to partially neutralize the direct effect of consumer pressure on toxic
releases, particularly for HAP. Finally, we find that innovative firms, irrespective of their
pollution intensity in the past, were making statistically significant reductions in their
HAP/SALES ratio. However, innovativeness did not have a significant impact on on-site
release intensity, off-site release intensity or on total releases/sales (by recalling the results of
Table 6). This suggests that innovative firms are driven to reduce the types of pollutants that are
more likely to be regulated in the future.
6. Conclusions
Firms are increasingly addressing environmental concerns in a more proactive manner through
the adoption of EMSs that integrate environmental considerations in various facets of
production. Regulators are seeking to encourage this trend towards self-regulation by providing
technical and financial assistance and through regulatory incentives. EMSs can differ considerably
among firms in the comprehensiveness of their coverage and the ambitiousness of their goals.
Analysis of the count of environmental practices adopted by S&P 500 firms shows that the threat
of liabilities and market-based pressures from consumers, investors and other firms are significant
motivators for the adoption of a more comprehensive EMS. Further, consumer pressure has a
stronger effect on firms that would have otherwise been adopters of a less comprehensive EMS
given their (other) characteristics.
We also find that the adoption of a more comprehensive EMS has a significant negative impact
on the intensity of toxic releases and that this impact is greater on firms that have inferior past
environmental records. In addition, we find a differential impact of these incentives on a firm’s
choice of pollution control method. Results show that EMSs have a negative effect on the
intensity of on-site releases and off-site transfers, though not on HAP per unit sales. These
findings suggest that adoption of EMSs leads to source reduction of total waste generation or to
pollution prevention and reduces end-of-pipe disposal. By and large, none of the market-based or
regulatory pressures considered are found to have had a significant direct impact on the pollution
intensity of firms. Rather, their effect is indirect and operates through inducing the adoption of a
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653
more comprehensive EMS. Our results, taken together, suggest that public policy can play a role
in inducing the prevention of toxic pollution by creating regulatory and market-based pressures
that induce adoption of EMSs. These pressures include a threat of stringent mandatory regulation
and the provision of environmental information about firms to the public. These results also
suggest that promoting the adoption of EMSs particularly by firms with large toxic release
intensity can be considered as an effective policy tool.
Acknowledgments
Senior authorship was not assigned. We would like to acknowledge financial support by the
University of Illinois Campus Research Board and by USEPA’s National Center for
Environmental Research, Science to Achieve Results (STAR) Program, Grant R827919-01.
Any opinions, findings, conclusions, or recommendations expressed in this publication are those
of the authors and do not necessarily reflect the view of the USEPA.
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